Overview

Dataset statistics

Number of variables24
Number of observations4061
Missing cells7569
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.6 KiB
Average record size in memory192.0 B

Variable types

Numeric14
Categorical10

Alerts

Age is highly overall correlated with Creatinine_ClearanceHigh correlation
Education is highly overall correlated with Work and 6 other fieldsHigh correlation
Year_DM_Diagnosed is highly overall correlated with DM_DurationHigh correlation
DM_Duration is highly overall correlated with Year_DM_DiagnosedHigh correlation
DM_Treatment is highly overall correlated with HbA1C_Admission_Value and 2 other fieldsHigh correlation
Waist is highly overall correlated with BMIHigh correlation
Fasting_Blood_Glucose_Value_SI_Units is highly overall correlated with HbA1C_Admission_Value and 1 other fieldsHigh correlation
HbA1C_Admission_Value is highly overall correlated with DM_Treatment and 1 other fieldsHigh correlation
BMI is highly overall correlated with WaistHigh correlation
Creatinine_Clearance is highly overall correlated with AgeHigh correlation
Work is highly overall correlated with Education and 7 other fieldsHigh correlation
Cardiac_Arrest_Admission is highly overall correlated with Education and 6 other fieldsHigh correlation
Non_Cardiac_Condition is highly overall correlated with Education and 7 other fieldsHigh correlation
Hypertension is highly overall correlated with Education and 7 other fieldsHigh correlation
Dyslipidemia is highly overall correlated with Education and 7 other fieldsHigh correlation
DM is highly overall correlated with Education and 10 other fieldsHigh correlation
DM_Type is highly overall correlated with DM_Treatment and 1 other fieldsHigh correlation
Smoking_History is highly overall correlated with Work and 5 other fieldsHigh correlation
Lipid_24_Collected is highly overall correlated with Education and 7 other fieldsHigh correlation
Cardiac_Arrest_Admission is highly imbalanced (91.7%)Imbalance
Non_Cardiac_Condition is highly imbalanced (87.6%)Imbalance
Lipid_24_Collected is highly imbalanced (60.8%)Imbalance
Year_DM_Diagnosed has 1871 (46.1%) missing valuesMissing
DM_Duration has 1975 (48.6%) missing valuesMissing
Waist has 92 (2.3%) missing valuesMissing
Fasting_Blood_Glucose_Value_SI_Units has 1289 (31.7%) missing valuesMissing
HbA1C_Admission_Value has 1499 (36.9%) missing valuesMissing
Cholesterol_Value_SI_Units has 334 (8.2%) missing valuesMissing
Triglycerides_Value_SI_Units has 374 (9.2%) missing valuesMissing
BMI has 60 (1.5%) missing valuesMissing
Creatinine_Clearance has 69 (1.7%) missing valuesMissing
DM_Duration is highly skewed (γ1 = 31.49782961)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Education has 969 (23.9%) zerosZeros
DM_Treatment has 103 (2.5%) zerosZeros

Reproduction

Analysis started2023-03-07 22:42:05.944029
Analysis finished2023-03-07 22:43:02.292272
Duration56.35 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4061
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2030
Minimum0
Maximum4060
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:02.416792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile203
Q11015
median2030
Q33045
95-th percentile3857
Maximum4060
Range4060
Interquartile range (IQR)2030

Descriptive statistics

Standard deviation1172.4541
Coefficient of variation (CV)0.57756357
Kurtosis-1.2
Mean2030
Median Absolute Deviation (MAD)1015
Skewness0
Sum8243830
Variance1374648.5
MonotonicityStrictly increasing
2023-03-07T22:43:02.736672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2713 1
 
< 0.1%
2700 1
 
< 0.1%
2701 1
 
< 0.1%
2702 1
 
< 0.1%
2703 1
 
< 0.1%
2704 1
 
< 0.1%
2705 1
 
< 0.1%
2706 1
 
< 0.1%
2707 1
 
< 0.1%
Other values (4051) 4051
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
4060 1
< 0.1%
4059 1
< 0.1%
4058 1
< 0.1%
4057 1
< 0.1%
4056 1
< 0.1%
4055 1
< 0.1%
4054 1
< 0.1%
4053 1
< 0.1%
4052 1
< 0.1%
4051 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2697 
0
1364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Length

2023-03-07T22:43:03.006394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:03.200979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring characters

ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2697
66.4%
0 1364
33.6%

Age
Real number (ℝ)

Distinct79
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.351884
Minimum18
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:03.395443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile40
Q152
median60
Q369
95-th percentile81
Maximum112
Range94
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.727595
Coefficient of variation (CV)0.21088977
Kurtosis-0.12256521
Mean60.351884
Median Absolute Deviation (MAD)9
Skewness0.0038224183
Sum245089
Variance161.99166
MonotonicityNot monotonic
2023-03-07T22:43:03.639251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 149
 
3.7%
72 138
 
3.4%
60 132
 
3.3%
57 132
 
3.3%
67 121
 
3.0%
59 119
 
2.9%
52 117
 
2.9%
56 114
 
2.8%
64 112
 
2.8%
70 111
 
2.7%
Other values (69) 2816
69.3%
ValueCountFrequency (%)
18 1
 
< 0.1%
23 1
 
< 0.1%
24 3
0.1%
25 6
0.1%
26 5
0.1%
27 4
0.1%
28 5
0.1%
29 5
0.1%
30 5
0.1%
31 7
0.2%
ValueCountFrequency (%)
112 1
 
< 0.1%
102 1
 
< 0.1%
99 5
0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 3
0.1%
94 6
0.1%
93 6
0.1%
92 6
0.1%
91 3
0.1%

Education
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9202167
Minimum0
Maximum6
Zeros969
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:03.806359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4284366
Coefficient of variation (CV)0.74389343
Kurtosis-0.55103223
Mean1.9202167
Median Absolute Deviation (MAD)1
Skewness0.32913589
Sum7798
Variance2.0404311
MonotonicityNot monotonic
2023-03-07T22:43:03.928493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1965
48.4%
0 969
23.9%
4 645
 
15.9%
1 249
 
6.1%
5 164
 
4.0%
3 65
 
1.6%
6 4
 
0.1%
ValueCountFrequency (%)
0 969
23.9%
1 249
 
6.1%
2 1965
48.4%
3 65
 
1.6%
4 645
 
15.9%
5 164
 
4.0%
6 4
 
0.1%
ValueCountFrequency (%)
6 4
 
0.1%
5 164
 
4.0%
4 645
 
15.9%
3 65
 
1.6%
2 1965
48.4%
1 249
 
6.1%
0 969
23.9%

Work
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
2923 
1
994 
2
 
140
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Length

2023-03-07T22:43:04.117860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:04.275142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2923
72.0%
1 994
 
24.5%
2 140
 
3.4%
3 4
 
0.1%

Cardiac_Arrest_Admission
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
3974 
1
 
73
2
 
10
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Length

2023-03-07T22:43:04.418146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:04.574049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3974
97.9%
1 73
 
1.8%
2 10
 
0.2%
3 4
 
0.1%

Non_Cardiac_Condition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
0
3943 
1
 
114
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Length

2023-03-07T22:43:04.733819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:04.958502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3943
97.1%
1 114
 
2.8%
2 4
 
0.1%

Hypertension
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2624 
0
1432 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Length

2023-03-07T22:43:05.121904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:05.299703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2624
64.6%
0 1432
35.3%
2 5
 
0.1%

Dyslipidemia
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2290 
0
1766 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Length

2023-03-07T22:43:05.423364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:05.600565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2290
56.4%
0 1766
43.5%
2 5
 
0.1%

DM
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2173 
0
1883 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Length

2023-03-07T22:43:05.822663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:06.013975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2173
53.5%
0 1883
46.4%
2 5
 
0.1%

Year_DM_Diagnosed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)3.3%
Missing1871
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean1905.1735
Minimum1
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:06.269760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1973
Q11994
median2002
Q32007
95-th percentile2011
Maximum2022
Range2021
Interquartile range (IQR)13

Descriptive statistics

Standard deviation424.59699
Coefficient of variation (CV)0.22286526
Kurtosis15.935654
Mean1905.1735
Median Absolute Deviation (MAD)5
Skewness-4.2324282
Sum4172330
Variance180282.6
MonotonicityNot monotonic
2023-03-07T22:43:06.537798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002 278
 
6.8%
2007 166
 
4.1%
1992 149
 
3.7%
1997 146
 
3.6%
2008 109
 
2.7%
2000 106
 
2.6%
2003 99
 
2.4%
2006 92
 
2.3%
2010 85
 
2.1%
2011 76
 
1.9%
Other values (62) 884
21.8%
(Missing) 1871
46.1%
ValueCountFrequency (%)
1 5
0.1%
2 5
0.1%
3 2
 
< 0.1%
4 3
 
0.1%
5 3
 
0.1%
6 3
 
0.1%
7 9
0.2%
8 3
 
0.1%
9 4
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2013 1
 
< 0.1%
2012 55
 
1.4%
2011 76
1.9%
2010 85
2.1%
2009 73
1.8%
2008 109
2.7%
2007 166
4.1%
2006 92
2.3%
2005 67
1.6%

DM_Duration
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct45
Distinct (%)2.2%
Missing1975
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean13.706616
Minimum-10
Maximum2004
Zeros30
Zeros (%)0.7%
Negative1
Negative (%)< 0.1%
Memory size31.9 KiB
2023-03-07T22:43:06.791937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile1
Q15.25
median10
Q316
95-th percentile27.75
Maximum2004
Range2014
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation61.986548
Coefficient of variation (CV)4.5223817
Kurtosis1007.2521
Mean13.706616
Median Absolute Deviation (MAD)5
Skewness31.49783
Sum28592
Variance3842.3321
MonotonicityNot monotonic
2023-03-07T22:43:07.002834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10 339
 
8.3%
20 198
 
4.9%
15 170
 
4.2%
5 169
 
4.2%
12 113
 
2.8%
6 100
 
2.5%
4 85
 
2.1%
2 83
 
2.0%
7 82
 
2.0%
8 79
 
1.9%
Other values (35) 668
 
16.4%
(Missing) 1975
48.6%
ValueCountFrequency (%)
-10 1
 
< 0.1%
0 30
 
0.7%
1 78
1.9%
2 83
2.0%
3 76
1.9%
4 85
2.1%
5 169
4.2%
6 100
2.5%
7 82
2.0%
8 79
1.9%
ValueCountFrequency (%)
2004 1
 
< 0.1%
1992 1
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
40 5
0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 7
0.2%
34 1
 
< 0.1%

DM_Type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2163 
2
1872 
0
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Length

2023-03-07T22:43:07.230308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:07.438682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2163
53.3%
2 1872
46.1%
0 26
 
0.6%

DM_Treatment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0076336
Minimum0
Maximum9
Zeros103
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:07.606454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.446107
Coefficient of variation (CV)0.34906319
Kurtosis0.50661024
Mean7.0076336
Median Absolute Deviation (MAD)1
Skewness-1.1742262
Sum28458
Variance5.9834393
MonotonicityNot monotonic
2023-03-07T22:43:07.756773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 1873
46.1%
7 896
22.1%
5 489
 
12.0%
3 338
 
8.3%
8 164
 
4.0%
0 103
 
2.5%
1 100
 
2.5%
4 63
 
1.6%
6 34
 
0.8%
2 1
 
< 0.1%
ValueCountFrequency (%)
0 103
 
2.5%
1 100
 
2.5%
2 1
 
< 0.1%
3 338
 
8.3%
4 63
 
1.6%
5 489
 
12.0%
6 34
 
0.8%
7 896
22.1%
8 164
 
4.0%
9 1873
46.1%
ValueCountFrequency (%)
9 1873
46.1%
8 164
 
4.0%
7 896
22.1%
6 34
 
0.8%
5 489
 
12.0%
4 63
 
1.6%
3 338
 
8.3%
2 1
 
< 0.1%
1 100
 
2.5%
0 103
 
2.5%

Smoking_History
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
2464 
0
982 
2
524 
3
 
85
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row0
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Length

2023-03-07T22:43:07.933308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:08.243491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2464
60.7%
0 982
 
24.2%
2 524
 
12.9%
3 85
 
2.1%
4 6
 
0.1%

Heart_Rate
Real number (ℝ)

Distinct140
Distinct (%)3.5%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.175832
Minimum0
Maximum222
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:08.443434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q171
median82
Q396
95-th percentile120
Maximum222
Range222
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.087922
Coefficient of variation (CV)0.24758105
Kurtosis3.2637604
Mean85.175832
Median Absolute Deviation (MAD)12
Skewness1.0140859
Sum345388
Variance444.70045
MonotonicityNot monotonic
2023-03-07T22:43:08.656800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 298
 
7.3%
90 203
 
5.0%
70 202
 
5.0%
100 151
 
3.7%
78 136
 
3.3%
88 126
 
3.1%
75 118
 
2.9%
72 105
 
2.6%
85 101
 
2.5%
76 88
 
2.2%
Other values (130) 2527
62.2%
ValueCountFrequency (%)
0 5
0.1%
8 1
 
< 0.1%
25 2
 
< 0.1%
27 1
 
< 0.1%
30 3
 
0.1%
34 2
 
< 0.1%
35 3
 
0.1%
36 2
 
< 0.1%
38 5
0.1%
40 11
0.3%
ValueCountFrequency (%)
222 1
 
< 0.1%
220 1
 
< 0.1%
200 2
< 0.1%
187 1
 
< 0.1%
180 4
0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
172 2
< 0.1%
170 3
0.1%
168 3
0.1%

Waist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct120
Distinct (%)3.0%
Missing92
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean97.592341
Minimum25
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:08.885522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile72
Q188
median98
Q3109
95-th percentile125
Maximum194
Range169
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.450379
Coefficient of variation (CV)0.1788089
Kurtosis2.3121858
Mean97.592341
Median Absolute Deviation (MAD)10
Skewness-0.35062131
Sum387344
Variance304.51573
MonotonicityNot monotonic
2023-03-07T22:43:09.116627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 225
 
5.5%
100 171
 
4.2%
85 158
 
3.9%
110 151
 
3.7%
98 145
 
3.6%
105 132
 
3.3%
80 127
 
3.1%
95 123
 
3.0%
88 123
 
3.0%
102 111
 
2.7%
Other values (110) 2503
61.6%
ValueCountFrequency (%)
25 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
30 2
 
< 0.1%
32 9
0.2%
33 4
0.1%
34 5
0.1%
35 1
 
< 0.1%
36 8
0.2%
37 1
 
< 0.1%
ValueCountFrequency (%)
194 1
 
< 0.1%
191 2
 
< 0.1%
165 1
 
< 0.1%
160 1
 
< 0.1%
155 1
 
< 0.1%
152 1
 
< 0.1%
150 4
0.1%
148 5
0.1%
147 1
 
< 0.1%
146 1
 
< 0.1%

Fasting_Blood_Glucose_Value_SI_Units
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct458
Distinct (%)16.5%
Missing1289
Missing (%)31.7%
Infinite0
Infinite (%)0.0%
Mean7.6690703
Minimum0.33
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:09.368652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile4.2
Q15.4
median6.7
Q39.2
95-th percentile14.245
Maximum20
Range19.67
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation3.2859569
Coefficient of variation (CV)0.42846875
Kurtosis1.5200427
Mean7.6690703
Median Absolute Deviation (MAD)1.6
Skewness1.2062127
Sum21258.663
Variance10.797513
MonotonicityNot monotonic
2023-03-07T22:43:09.580704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 79
 
1.9%
5.2 76
 
1.9%
5.6 68
 
1.7%
5 68
 
1.7%
4.8 55
 
1.4%
5.4 53
 
1.3%
6.2 53
 
1.3%
5.8 53
 
1.3%
5.5 52
 
1.3%
5.7 49
 
1.2%
Other values (448) 2166
53.3%
(Missing) 1289
31.7%
ValueCountFrequency (%)
0.33 1
< 0.1%
0.38 1
< 0.1%
0.46 1
< 0.1%
0.47 1
< 0.1%
0.58 1
< 0.1%
0.61 1
< 0.1%
0.71 1
< 0.1%
0.81 1
< 0.1%
0.98 1
< 0.1%
1.01 1
< 0.1%
ValueCountFrequency (%)
20 3
0.1%
19.7 3
0.1%
19.6 1
 
< 0.1%
19.5 1
 
< 0.1%
19.4 2
< 0.1%
19.25 1
 
< 0.1%
19.2 1
 
< 0.1%
19.1 1
 
< 0.1%
19 3
0.1%
18.9 1
 
< 0.1%

HbA1C_Admission_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct384
Distinct (%)15.0%
Missing1499
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean7.5797931
Minimum0
Maximum88.4
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:09.818409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9
Q15.9
median7
Q38.9225
95-th percentile11.9095
Maximum88.4
Range88.4
Interquartile range (IQR)3.0225

Descriptive statistics

Standard deviation2.7954479
Coefficient of variation (CV)0.36880267
Kurtosis272.19291
Mean7.5797931
Median Absolute Deviation (MAD)1.37
Skewness9.9580976
Sum19419.43
Variance7.814529
MonotonicityNot monotonic
2023-03-07T22:43:10.036700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 144
 
3.5%
5.9 71
 
1.7%
5.7 69
 
1.7%
5.8 65
 
1.6%
5.3 53
 
1.3%
5.6 52
 
1.3%
5.5 52
 
1.3%
7 49
 
1.2%
5 48
 
1.2%
5.4 48
 
1.2%
Other values (374) 1911
47.1%
(Missing) 1499
36.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
3.2 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 1
 
< 0.1%
3.75 1
 
< 0.1%
3.9 12
0.3%
4 23
0.6%
4.01 1
 
< 0.1%
4.1 4
 
0.1%
ValueCountFrequency (%)
88.4 1
< 0.1%
20 1
< 0.1%
17.5 1
< 0.1%
16.6 1
< 0.1%
16 2
< 0.1%
15.8 1
< 0.1%
15.7 2
< 0.1%
15.4 1
< 0.1%
15.3 1
< 0.1%
15.2 2
< 0.1%

Lipid_24_Collected
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
1
3455 
0
599 
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4061
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Length

2023-03-07T22:43:10.259271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:43:10.444672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3455
85.1%
0 599
 
14.8%
2 7
 
0.2%
Distinct555
Distinct (%)14.9%
Missing334
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean4.7274624
Minimum0.07
Maximum15.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:10.643091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile2.83
Q13.8
median4.6
Q35.505
95-th percentile7
Maximum15.7
Range15.63
Interquartile range (IQR)1.705

Descriptive statistics

Standard deviation1.3533624
Coefficient of variation (CV)0.28627671
Kurtosis3.086324
Mean4.7274624
Median Absolute Deviation (MAD)0.88
Skewness0.80137913
Sum17619.252
Variance1.8315897
MonotonicityNot monotonic
2023-03-07T22:43:10.914225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 82
 
2.0%
4.5 80
 
2.0%
5.2 74
 
1.8%
4.6 73
 
1.8%
4.2 73
 
1.8%
5.6 70
 
1.7%
4.1 67
 
1.6%
4.9 67
 
1.6%
4 66
 
1.6%
4.8 65
 
1.6%
Other values (545) 3010
74.1%
(Missing) 334
 
8.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.08 3
0.1%
0.1 2
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 2
< 0.1%
0.18 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
15.7 1
< 0.1%
13.74 1
< 0.1%
12.64554 1
< 0.1%
11.8 2
< 0.1%
11.36 1
< 0.1%
11.29 1
< 0.1%
10.97 1
< 0.1%
10.12 1
< 0.1%
10 1
< 0.1%
9.91 1
< 0.1%
Distinct432
Distinct (%)11.7%
Missing374
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean1.6549231
Minimum0.01
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:11.160852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.59
Q10.96
median1.4
Q32
95-th percentile3.591
Maximum17.6
Range17.59
Interquartile range (IQR)1.04

Descriptive statistics

Standard deviation1.1592965
Coefficient of variation (CV)0.70051378
Kurtosis31.752659
Mean1.6549231
Median Absolute Deviation (MAD)0.5
Skewness3.9022952
Sum6101.7016
Variance1.3439683
MonotonicityNot monotonic
2023-03-07T22:43:11.385282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 108
 
2.7%
1.1 107
 
2.6%
1.2 107
 
2.6%
1.3 102
 
2.5%
0.8 99
 
2.4%
0.9 98
 
2.4%
1.5 97
 
2.4%
1.4 91
 
2.2%
1.6 91
 
2.2%
1.8 80
 
2.0%
Other values (422) 2707
66.7%
(Missing) 374
 
9.2%
ValueCountFrequency (%)
0.01 6
0.1%
0.02 3
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.1 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.4 1
< 0.1%
14.55 1
< 0.1%
11.56 1
< 0.1%
11.01 1
< 0.1%
10.59 1
< 0.1%
9.72 1
< 0.1%
9.14 1
< 0.1%
9 1
< 0.1%
8.88 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1187
Distinct (%)29.7%
Missing60
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean29.011505
Minimum13.34
Maximum342.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:11.582522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13.34
5-th percentile21.48
Q124.91
median27.76
Q331.59
95-th percentile39.54
Maximum342.78
Range329.44
Interquartile range (IQR)6.68

Descriptive statistics

Standard deviation8.9093348
Coefficient of variation (CV)0.30709661
Kurtosis507.7302
Mean29.011505
Median Absolute Deviation (MAD)3.3
Skewness16.91749
Sum116075.03
Variance79.376246
MonotonicityNot monotonic
2023-03-07T22:43:11.791506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.71 40
 
1.0%
27.68 38
 
0.9%
24.22 36
 
0.9%
29.41 30
 
0.7%
29.76 28
 
0.7%
31.25 27
 
0.7%
27.34 27
 
0.7%
25.95 26
 
0.6%
31.22 25
 
0.6%
28.73 23
 
0.6%
Other values (1177) 3701
91.1%
(Missing) 60
 
1.5%
ValueCountFrequency (%)
13.34 1
< 0.1%
14.84 1
< 0.1%
15.78 1
< 0.1%
16.42 2
< 0.1%
16.44 1
< 0.1%
16.77 1
< 0.1%
16.82 2
< 0.1%
16.9 1
< 0.1%
17.04 1
< 0.1%
17.07 1
< 0.1%
ValueCountFrequency (%)
342.78 1
< 0.1%
233.4 1
< 0.1%
221.14 1
< 0.1%
96.88 1
< 0.1%
69.25 1
< 0.1%
66.33 1
< 0.1%
65.38 1
< 0.1%
65.02 1
< 0.1%
63.57 1
< 0.1%
61.33 1
< 0.1%

Creatinine_Clearance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3429
Distinct (%)85.9%
Missing69
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean88.409218
Minimum3.7
Maximum1164.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:43:12.057413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile25.551
Q157.165
median82.865
Q3110.9875
95-th percentile167.3145
Maximum1164.7
Range1161
Interquartile range (IQR)53.8225

Descriptive statistics

Standard deviation53.172312
Coefficient of variation (CV)0.60143403
Kurtosis101.67909
Mean88.409218
Median Absolute Deviation (MAD)26.955
Skewness6.1454723
Sum352929.6
Variance2827.2948
MonotonicityNot monotonic
2023-03-07T22:43:12.395187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.1 6
 
0.1%
83.2 6
 
0.1%
76.16 4
 
0.1%
79.9 4
 
0.1%
76.26 4
 
0.1%
67.65 4
 
0.1%
110.7 4
 
0.1%
97.17 4
 
0.1%
98.4 3
 
0.1%
76.88 3
 
0.1%
Other values (3419) 3950
97.3%
(Missing) 69
 
1.7%
ValueCountFrequency (%)
3.7 1
< 0.1%
5.21 1
< 0.1%
5.24 1
< 0.1%
5.57 1
< 0.1%
5.7 1
< 0.1%
5.98 1
< 0.1%
6.18 1
< 0.1%
6.23 1
< 0.1%
6.29 1
< 0.1%
6.33 1
< 0.1%
ValueCountFrequency (%)
1164.7 1
< 0.1%
1153.62 1
< 0.1%
875.43 1
< 0.1%
684.96 1
< 0.1%
626.18 1
< 0.1%
500.2 1
< 0.1%
460.63 1
< 0.1%
363.73 1
< 0.1%
321.8 1
< 0.1%
298.68 1
< 0.1%

Interactions

2023-03-07T22:42:57.094451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:08.630134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:13.608364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:17.998507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:21.381434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:25.611499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:30.780184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:33.985359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:38.329432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:41.186209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:43.956700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:47.226610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:50.600956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:53.786781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:57.425676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:08.859473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:13.905592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:18.326452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:21.728440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:26.036480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:31.012077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:34.192245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:38.609931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:41.408201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:44.205227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:47.454065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:50.834886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:54.094569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:57.655562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:09.093276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:14.236690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:18.561219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:22.038367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:26.392511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:31.210298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:34.422691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:38.804793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:41.639961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:44.392140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:47.763283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:51.149918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:54.277047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:57.867337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:09.421597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:14.627214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:18.830142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:22.274252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:26.864032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:31.389678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:34.754594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:39.005939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:41.818961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:44.573321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:47.983065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:51.386274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:54.454818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:58.064921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:09.879788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:14.965456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:19.082394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:22.570286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:27.362369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:31.622613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:35.141647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:39.188056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:42.029769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:44.751986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:48.205488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:51.611607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:54.721729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:58.318337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:10.249334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:15.259074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:19.324113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:22.840279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:27.872666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:31.846017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:35.600451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:39.425064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:42.224023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:44.938828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:48.519510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:51.890806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:54.946001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:58.520106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:10.879056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:15.543329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:19.606049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:23.036716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:28.114476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:32.086391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:35.817162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:39.593252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:42.426044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:45.266411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:48.707134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:52.122177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:55.168869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:58.728377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:11.176434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:15.835168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:19.889900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:23.284472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:28.445269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:32.379398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:36.025451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:39.787155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:42.612161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:45.467519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:48.923963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:52.316996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:55.324877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:58.970344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:11.510311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:16.115542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:20.129505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:23.542350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:28.821294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:32.619534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:36.435932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:40.002211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:42.799322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:45.633875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:49.167303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:52.538111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:55.544183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:59.180643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:11.921686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:16.525678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:20.361447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:23.826988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:29.220620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:32.854881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:36.808053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:40.213411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:43.004941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:45.871657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:49.460011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:52.738136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:55.741545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:59.380335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:12.169019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:16.893190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:20.564119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:24.063146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:29.477663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:33.041722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:37.230527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:40.404592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:43.186600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:46.046949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:49.659727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:52.933460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:55.938579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:59.766977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:12.510032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:17.220314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:20.763909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:24.392270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:42:43.357151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:42:53.157935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:42:24.930251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:30.168162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:33.512584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:37.825292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:40.840254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:42:46.568232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:50.122792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:53.377908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:56.371485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:42:17.747174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:21.180395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:25.224809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:30.524671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:33.732877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:38.110738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:41.018187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:43.757720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:46.910344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:50.316273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:53.587401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:42:56.730571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-07T22:43:12.601488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0AgeEducationYear_DM_DiagnosedDM_DurationDM_TreatmentHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_ClearanceGenderWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMDM_TypeSmoking_HistoryLipid_24_Collected
Unnamed: 01.000-0.024-0.009-0.0170.0140.037-0.037-0.014-0.029-0.063-0.030-0.0460.0440.0310.1410.1020.1160.1210.1310.1060.0740.0630.1230.109
Age-0.0241.000-0.034-0.2780.301-0.1100.040-0.0980.0440.029-0.176-0.226-0.154-0.6390.1620.3270.0570.0660.2050.1210.1400.1630.1610.057
Education-0.009-0.0341.0000.020-0.007-0.0070.0070.004-0.028-0.0130.019-0.0170.0320.0320.3530.6550.5770.7070.6420.6330.6340.0740.4420.534
Year_DM_Diagnosed-0.017-0.2780.0201.000-0.995-0.030-0.067-0.0260.028-0.0670.1760.070-0.1000.2040.0170.0220.0000.0000.0000.0060.0000.0610.0640.035
DM_Duration0.0140.301-0.007-0.9951.0000.0540.0510.044-0.0180.075-0.179-0.0700.108-0.2200.0000.0000.0000.0000.0000.0220.0780.4990.0000.000
DM_Treatment0.037-0.110-0.007-0.0300.0541.000-0.098-0.132-0.455-0.6000.105-0.091-0.1210.0960.2000.1130.0110.0190.2370.2370.7000.7130.0650.037
Heart_Rate-0.0370.0400.007-0.0670.051-0.0981.000-0.0090.0870.1120.014-0.0050.047-0.0600.1170.0470.1760.1580.0910.0000.1020.0900.0390.000
Waist-0.014-0.0980.004-0.0260.044-0.132-0.0091.0000.1110.105-0.0050.1300.5090.2580.1210.0550.0280.0000.1070.1180.1160.0830.0650.072
Fasting_Blood_Glucose_Value_SI_Units-0.0290.044-0.0280.028-0.018-0.4550.0870.1111.0000.5990.0160.1390.118-0.0030.1160.0930.0250.0340.1580.1600.5690.4050.0510.038
HbA1C_Admission_Value-0.0630.029-0.013-0.0670.075-0.6000.1120.1050.5991.000-0.0050.1850.1430.0060.1040.0270.0000.0000.1080.1430.4520.3180.0370.000
Cholesterol_Value_SI_Units-0.030-0.1760.0190.176-0.1790.1050.014-0.0050.016-0.0051.0000.3870.0340.1460.0420.0760.0000.0540.1150.0690.1270.0970.0470.085
Triglycerides_Value_SI_Units-0.046-0.226-0.0170.070-0.070-0.091-0.0050.1300.1390.1850.3871.0000.1560.1580.0000.0940.0000.0000.0000.0610.0840.0750.0650.063
BMI0.044-0.1540.032-0.1000.108-0.1210.0470.5090.1180.1430.0340.1561.0000.3820.0700.0000.0160.0000.0150.0000.0130.0000.0090.000
Creatinine_Clearance0.031-0.6390.0320.204-0.2200.096-0.0600.258-0.0030.0060.1460.1580.3821.0000.0210.2050.0000.0570.1260.0610.0600.0530.1290.000
Gender0.1410.1620.3530.0170.0000.2000.1170.1210.1160.1040.0420.0000.0700.0211.0000.3800.0000.0000.2070.1200.1620.1630.4330.064
Work0.1020.3270.6550.0220.0000.1130.0470.0550.0930.0270.0760.0940.0000.2050.3801.0000.5770.7080.6550.6380.6430.1260.5020.535
Cardiac_Arrest_Admission0.1160.0570.5770.0000.0000.0110.1760.0280.0250.0000.0000.0000.0160.0000.0000.5771.0000.7100.6320.6330.6320.0100.4710.534
Non_Cardiac_Condition0.1210.0660.7070.0000.0000.0190.1580.0000.0340.0000.0540.0000.0000.0570.0000.7080.7101.0000.6330.6320.6330.0320.5780.535
Hypertension0.1310.2050.6420.0000.0000.2370.0910.1070.1580.1080.1150.0000.0150.1260.2070.6550.6320.6331.0000.7600.7450.2330.6580.597
Dyslipidemia0.1060.1210.6330.0060.0220.2370.0000.1180.1600.1430.0690.0610.0000.0610.1200.6380.6330.6320.7601.0000.7440.2300.6500.597
DM0.0740.1400.6340.0000.0780.7000.1020.1160.5690.4520.1270.0840.0130.0600.1620.6430.6320.6330.7450.7441.0000.7010.6510.597
DM_Type0.0630.1630.0740.0610.4990.7130.0900.0830.4050.3180.0970.0750.0000.0530.1630.1260.0100.0320.2330.2300.7011.0000.0850.004
Smoking_History0.1230.1610.4420.0640.0000.0650.0390.0650.0510.0370.0470.0650.0090.1290.4330.5020.4710.5780.6580.6500.6510.0851.0000.654
Lipid_24_Collected0.1090.0570.5340.0350.0000.0370.0000.0720.0380.0000.0850.0630.0000.0000.0640.5350.5340.5350.5970.5970.5970.0040.6541.000

Missing values

2023-03-07T22:43:00.727711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-07T22:43:01.429166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-07T22:43:01.879123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0GenderAgeEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMYear_DM_DiagnosedDM_DurationDM_TypeDM_TreatmentSmoking_HistoryHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueLipid_24_CollectedCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_Clearance
001621100222NaNNaN294NaNNaNNaNNaN2NaNNaNNaNNaN
1115211001112003.010.0134NaNNaNNaNNaN2NaNNaNNaNNaN
220730000110NaNNaN29075.060.07.00NaN15.381.8520.0024.12
331466332222NaNNaN294NaNNaNNaNNaN2NaNNaNNaNNaN
440466332222NaNNaN294NaNNaNNaNNaN2NaNNaNNaNNaN
5506440001112002.010.0151117.085.011.93NaN15.402.2433.2963.60
6605600000111988.024.018182.085.04.407.104.701.3031.22114.24
770780000100NaNNaN29190.090.06.205.604.111.1119.5627.55
881542000100NaNNaN29175.091.04.005.615.202.2023.0592.37
990542000000NaNNaN29190.093.0NaNNaN0NaNNaN23.8173.42
Unnamed: 0GenderAgeEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMYear_DM_DiagnosedDM_DurationDM_TypeDM_TreatmentSmoking_HistoryHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueLipid_24_CollectedCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_Clearance
4051405117400001111987.025.011188.0NaN10.88.315.141.1028.4156.50
405240521764100100NaNNaN290140.070.0NaNNaN0NaNNaN25.7134.66
4053405316152011111993.020.017187.090.04.27.415.102.5029.7650.65
4054405405620001012004.08.013162.0115.07.28.107.442.3233.12109.82
4055405514101001112006.07.0172106.079.0NaNNaN15.901.9027.9788.77
4056405617520001112001.011.015198.092.07.58.814.672.2223.5145.28
4057405715600200111996.015.0170120.093.07.911.616.661.5329.3983.77
4058405815540000017.0NaN170109.072.09.211.613.950.8022.8489.61
405940591772000100NaNNaN291100.085.0NaNNaN16.710.6522.0431.00
4060406017320001111987.025.0180100.0117.0NaNNaN14.101.7729.0167.36